code: def griffin_lim_vocode(
      mel_specgram,
      n_fft,
      n_mels,
      sample_rate,
      mel_fmin,
      mel_fmax,
      jit,
  ):
      from torchaudio.transforms import GriffinLim, InverseMelScale
  
      inv_norm = InverseSpectralNormalization()
      inv_mel = InverseMelScale(
          n_stft=(n_fft // 2 + 1),
          n_mels=n_mels,
          sample_rate=sample_rate,
          f_min=mel_fmin,
          f_max=mel_fmax,
          mel_scale="slaney",
          norm="slaney",
      )
      griffin_lim = GriffinLim(
          n_fft=n_fft,
          power=1,
          hop_length=256,
          win_length=1024,
      )
  
      vocoder = torch.nn.Sequential(inv_norm, inv_mel, griffin_lim)
  
      if jit:
          vocoder = torch.jit.script(vocoder)
  
      waveform = vocoder(mel_specgram.cpu())
      return waveform
  
file_path: audio/examples/pipeline_tacotron2/inference.py
imports: import argparse
  import os
  import random
  import sys
  from functools import partial
  import numpy
  import torch
  import torchaudio
  from datasets import InverseSpectralNormalization
  from text.text_preprocessing import available_phonemizers, available_symbol_set, get_symbol_list, text_to_sequence
  from torchaudio.models import Tacotron2, tacotron2
  from utils import prepare_input_sequence
label_desc: Use image node_id 5 for calling a global function with example usage: griffin_lim_vocode(mel_specgram, n_fft, n_mels, sample_rate, mel_fmin, mel_fmax, jit) and returns: waveform